research
∙
07/01/2023
One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale
Large-scale medical imaging datasets have accelerated development of art...
share
research
∙
06/08/2023
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
While Deep Reinforcement Learning has been widely researched in medical ...
research
∙
05/31/2023
Multi-environment lifelong deep reinforcement learning for medical imaging
Deep reinforcement learning(DRL) is increasingly being explored in medic...
research
∙
05/24/2023
High-Throughput AI Inference for Medical Image Classification and Segmentation using Intelligent Streaming
As the adoption of AI systems within the clinical setup grows, limitatio...
research
∙
05/12/2023
Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery
The Imaging Data Commons (IDC) is a cloud-based database that provides r...
research
∙
03/12/2023
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Federated learning is a recent development in the machine learning area ...
research
∙
03/10/2023
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels
Numerous large-scale chest x-ray datasets have spearheaded expert-level ...
research
∙
02/22/2023
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Selective experience replay is a popular strategy for integrating lifelo...
research
∙
01/17/2023
SegViz: A Federated Learning Framework for Medical Image Segmentation from Distributed Datasets with Different and Incomplete Annotations
Segmentation is one of the primary tasks in the application of deep lear...
research
∙
01/17/2023
Surgical Aggregation: A Federated Learning Framework for Harmonizing Distributed Datasets with Diverse Tasks
AI-assisted characterization of chest x-rays (CXR) has the potential to ...
research
∙
11/11/2022
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data s...
research
∙
12/18/2021
Cross-Domain Federated Learning in Medical Imaging
Federated learning is increasingly being explored in the field of medica...
research
∙
08/01/2019
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
A current clinical challenge is identifying limb girdle muscular dystrop...
research
∙
06/10/2019
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Radiomics is an exciting new area of texture research for extracting qua...
research
∙
10/25/2018
Radiomic Synthesis Using Deep Convolutional Neural Networks
Radiomics is a rapidly growing field that deals with modeling the textur...
research
∙
09/25/2018
MPRAD: A Multiparametric Radiomics Framework
Multiparametric radiological imaging is vital for detection, characteriz...
research
∙
06/13/2016
